Research on the correlation between digital economy and carbon emission based on PSO-Kmeans algorithm

Based on the PSO-Kmeans algorithm, this paper deeply explores the correlation between digital economy and carbon emissions in China's prefecture-level cities. Firstly, random forest algorithm is used to deal with missing values, logarithmic transformation is used to improve data skew distribution, and principal component analysis is used to reduce data dimension, which improves the quality of data analysis. Furthermore, K-means clustering algorithm combined with Hopkins statistics was used to verify the feasibility of clustering. Monte Carlo simulation and PSO-Kmeans algorithm were used to optimize the clustering results, and the type characteristics of carbon emission trends in each city were obtained.